Abstract

Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal that the swarm was going towards into a new goal due to dynamic changes in mission objectives. There are two related but distinct capabilities needed to supervise a robotic swarm. The first is comprehension of the swarm's state and the second is prediction of the effects of human inputs on the swarm's behavior. Both of them are very challenging. Prior work in the literature has shown that inserting the human input as soon as possible to divert the swarm from its original goal towards the new goal does not always result in optimal performance (measured by some criterion such as the total time required by the swarm to reach the second goal). This phenomenon has been called Neglect Benevolence, conveying the idea that in many cases it is preferable to neglect the swarm for some time before inserting human input. In this paper, we study how humans can develop an understanding of swarm dynamics so they can predict the effects of the timing of their input on the state and performance of the swarm. We developed the swarm configuration shape-changing Neglect Benevolence Task as a Human Swarm Interaction (HSI) reference task allowing comparison between human and optimal input timing performance in control of swarms. Our results show that humans can learn to approximate optimal timing and that displays which make consensus variables perceptually accessible can enhance performance.